Preoperative Contrast-Enhanced CT-Based Deep Learning Radiomics Model for Distinguishing Retroperitoneal Lipomas and Well‑Differentiated Liposarcomas.

Journal: Academic radiology
Published Date: Jul 14, 2024

Abstract

RATIONALE AND OBJECTIVES: To assess the efficacy of a preoperative contrast-enhanced CT (CECT)-based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroperitoneal well-differentiated liposarcomas (WDLPS) and lipomas.

Authors

  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Lei Miao
    School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China. Electronic address: miaolei@seu.edu.cn.
  • Chen-Xi Wang
    Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang University School of Medicine, Nanchang, Jiangxi 330006, China.
  • Hong-Hao Wang
    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China.
  • Qi-Zheng Wang
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China (J.X., C.X.W., H.H.W., Q.Z.W., N.L.).
  • Meng Li
    Co-Innovation Center for the Sustainable Forestry in Southern China; Cerasus Research Center; College of Biology and the Environment, Nanjing Forestry University, Nanjing, China.
  • Hai-Song Chen
    Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, Shandong 266003, China (H.S.C.).
  • Ning Lang
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.

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